Medical image data obtained by scanning a patient using a medical image imaging device (also referred to as a modality) may be displayed on a display as an image after undergoing image processing. The medical image imaging device herein includes, for example, an ultrasound diagnostic apparatus, an X-ray diagnostic apparatus, an MRI apparatus (Magnetic Resonance Imaging), an X-ray CT apparatus, etc. These medical image imaging devices and medical image processing systems include a server comprising an image processor that processes images with respect to the image data.
The server carrying out image processing on the image data executes multiple image processes in parallel within its permissible processing capacity range (for example, the throughput of CPU, memory, etc.), thereby shortening the processing time of multiple image processing.
However, the throughput of image processing that may be carried out using a single server is restricted by the processing capacity of the server. Accordingly, when the throughput of the image processing required for the entire system exceeds the throughput of one server, there are cases in which a method is used of sharing image processing between a plurality of servers. In this way, the processing load of each server may be reduced, and the maximum simultaneous feasible throughput may be increased.
When sharing image processing between a plurality of servers, the maximum throughput is predicted based on the predicted number of reserved scans using a scanning apparatus (the number of reserved scans) and the predicted amount of image data to be acquired, after which the number of servers to be installed is determined in correspondence with this maximum throughput.
However, the number of reserved scans and the amount of image data to be scanned is not always constant. For example, the throughput required for the system temporarily increases at periods and times when reservations for scanning concentrate. Moreover, the amount of image data differs depending on the test conditions; for example, when there is a concentration of tests accompanying scanning large images and scanning with numerous scans, the throughput required for the system temporarily increases. In the conventional method, the maximum throughput was predicted including load in such non-ready states (hereinafter, referred to as “non-ready state load”), and more servers were installed as needed.
However, when the incidence of a non-ready state load is low, the resource of the servers increased upon this prediction cannot be effectively used, and the cost benefit from increasing servers is low. Moreover, the non-ready state load is caused by uncertain elements such as the number of reserved scans, scanning conditions, etc., making it difficult to predict correct values in advance.